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Title:
user graph regularized pairwise matrix factorization for item recommendation
Author: Du Liang ; Li Xuan ; Shen Yi-Dong
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference Name: 7th International Conference on Advanced Data Mining and Applications, ADMA 2011
Conference Date: December 1
Issued Date: 2011
Conference Place: Beijing, China
Keyword: Data mining ; Factorization
Indexed Type: EI
ISSN: 0302-9743
ISBN: 9783642258558
Department: (1) State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China; (2) Graduate University Chinese Academy of Sciences Beijing 100049 China
Sponsorship: IBM Research; China Samsung Telecom R and D Center; Tsinghua University
Abstract: Item recommendation from implicit, positive only feedback is an emerging setup in collaborative filtering in which only one class examples are observed. In this paper, we propose a novel method, called User Graph regularized Pairwise Matrix Factorization (UGPMF), to seamlessly integrate user information into pairwise matrix factorization procedure. Due to the use of the available information on user side, we are able to find more compact, low dimensional representations for users and items. Experiments on real-world recommendation data sets demonstrate that the proposed method significantly outperforms various competing alternative methods on top-k ranking performance of one-class item recommendation task. © 2011 Springer-Verlag.
English Abstract: Item recommendation from implicit, positive only feedback is an emerging setup in collaborative filtering in which only one class examples are observed. In this paper, we propose a novel method, called User Graph regularized Pairwise Matrix Factorization (UGPMF), to seamlessly integrate user information into pairwise matrix factorization procedure. Due to the use of the available information on user side, we are able to find more compact, low dimensional representations for users and items. Experiments on real-world recommendation data sets demonstrate that the proposed method significantly outperforms various competing alternative methods on top-k ranking performance of one-class item recommendation task. © 2011 Springer-Verlag.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16276
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Du Liang,Li Xuan,Shen Yi-Dong. user graph regularized pairwise matrix factorization for item recommendation[C]. 见:7th International Conference on Advanced Data Mining and Applications, ADMA 2011. Beijing, China. December 1.
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